Evidence map›Paper›PMID 42717238›Full record

ReviewNature protocols2026

netneurotools: a trainee-oriented approach to network neuroscience.

Zhen-Qi Liu, Vincent Bazinet, Justine Y Hansen, Filip Milisav, Andrea I Luppi, Eric G Ceballos, Asa Farahani, Laura E Suarez, Golia Shafiei, Ross D Markello and 1 more

Abstract readReview
PubMed Publisher
In one paragraph

Review in Nature protocols, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Spatially structured heterogeneity shapes large-scale cortical dynamics in a model of the human cortex.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  2. Article
  3. Article
  4. Structure-function coupling in the human brainstem.bioRxiv : the preprint server for biology · 2026
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Zhen-Qi Liu *Montréal Neurological Institute, McGill University, Montréal, Quebec, Canada.ORCID http://orcid.org/0000-0003-0917-7905
Vincent Bazinet *Montréal Neurological Institute, McGill University, Montréal, Quebec, Canada.ORCID http://orcid.org/0000-0001-6693-9641
Justine Y HansenMontréal Neurological Institute, McGill University, Montréal, Quebec, Canada.ORCID http://orcid.org/0000-0003-3142-7480
Filip MilisavMontréal Neurological Institute, McGill University, Montréal, Quebec, Canada.ORCID http://orcid.org/0000-0003-4685-3247
Andrea I LuppiMontréal Neurological Institute, McGill University, Montréal, Quebec, Canada.
Eric G CeballosMontréal Neurological Institute, McGill University, Montréal, Quebec, Canada.ORCID http://orcid.org/0000-0002-5643-1243
Asa FarahaniMontréal Neurological Institute, McGill University, Montréal, Quebec, Canada.ORCID http://orcid.org/0009-0001-9905-8813
Laura E SuarezMontréal Neurological Institute, McGill University, Montréal, Quebec, Canada.
Golia ShafieiMontréal Neurological Institute, McGill University, Montréal, Quebec, Canada.
Ross D MarkelloMontréal Neurological Institute, McGill University, Montréal, Quebec, Canada.
Bratislav MisicMontréal Neurological Institute, McGill University, Montréal, Quebec, Canada. bratislav.misic@mcgill.ca.ORCID http://orcid.org/0000-0003-0307-2862

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Brain imaging is an increasingly interdisciplinary field, encompassing multiple data types and multiple analytic traditions. Projects typically involve many moving parts, such as building customized preprocessing pipelines, transforming between data formats, preparing datasets for analysis and, ultimately, displaying results. The field is conventionally built on highly specialized software packages that solve these individual challenges well but are not necessarily designed to be interoperable. Trainees new to the field are therefore often left to come up with isolated heuristics and workarounds to complete a project. Here we present a set of tools to navigate the increasingly complex informatics ecosystem of brain imaging. netneurotools is a Python toolkit that has been continuously developed and maintained by this laboratory's trainees. The philosophy of the toolkit is that it should be the Swiss army knife of the laboratory: functions and routines that we often use but that are not part of any established pipeline or package. Since its inception, the toolkit has been open and welcomes contributions from neuroscientists across the globe. netneurotools presents a necessary counterweight to out-of-the-box software packages and highlights the importance of smaller, ad hoc functions for implementing projects. By opening a window into the inner workings of a laboratory, netneurotools also presents an opportunity to begin a new type of discourse among groups and establish tangible links within the community.

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.